Transforming Insights: AI’s New Role in Market Research
The advent of mainstream access to AI has sparked new possibilities across marketing disciplines — including Market Research, a discipline fundamentally rooted in learning from human perspectives. As AI becomes increasingly integrated into our daily lives, the Market Research team at Trajectory Data has assessed the most transformative ways that AI can be used to enhance traditional research methodologies, while preserving the human element that remains crucial to the field.
6 Ways AI is Transforming Market Research
Humanizing the respondent experience
It may seem counter-intuitive, but AI-driven tools can assist researchers in interacting with respondents in a more natural, human-like way. Once researchers have designed their study and have formulated draft questions, tools like ChatGPT do a great job of re-articulating verbiage in an engaging voice and tone that more naturally aligns the way that respondents think and interact with others. These tools can also do a great job in assisting with brainstorming methods to gamify stiff, standard research questions. While the initial output of these tools will likely miss the mark on nuances, clarity and precision, it is often full of inspiration for language that holds a respondent’s attention without making them feel ‘studied’ in the way that traditional research instruments often do.
AI probing for enhanced quantitative insights
Open-ended questions in quantitative surveys are a useful way to generate top-of-mind perspectives from a large set of respondents but often yield many frustratingly vague responses. Qualitative experience shows us that with simple probing, we can often obtain the clarity needed to understand the ‘why’ behind the ‘what.’ While advanced survey branching with Boolean logic was achievable in the past, it simply was not scalable or robust enough to generate authentic conversational lines of questioning. We used Open AI’s technology in our quantitative questionnaires in a way that enables us to ask each respondent follow-up questions in language that feels natural and encourages respondent elaboration on key areas of interest. While this will not replace the value of deep and rich feedback from qualitative studies, we do believe that it will start to bridge the gap between the two by yielding richer unexpected insights from quantitative studies.
Analyzing & minimizing biases in research instruments
While AI is not without its own inherent biases, it can be used as an additional tool to augment existing efforts toward minimizing biases in our research instruments. Tools like Chat GPT and Perplexity AI are useful secondary checks to identify potential sources of biases in questionnaire and discussion guide drafts, while also providing options to enhance representation and inclusivity. AI image generators are another great set of resources that have enabled us to ensure representation and minimize bias in our stimuli shown to respondents. Using image generators, we mitigate the limitations and variation in stock photography to create inclusive stimuli libraries that maintain a consistent look and feel.
Streamlining operations with coding and analytics techniques
The release of ChatGPT’s Advanced Data Analysis and similar tools have empowered research analysts with the ability to perform increasingly complex survey scripting, data manipulation and analytic techniques without necessary reliance on data scientists and developers. Enablement of analysts with advanced coding and scripting capabilities unlocks the door for the application of innovative techniques that may not be widely available in market research statistical analysis software while keeping the analysis in the hands of those who understand each specific element of the survey & dataset best.
Enable greater scale in qualitative participation with new analytic efficiencies
While AI will not replace the role of strategic researchers in developing and gleaning insights from qualitative studies, it does offer considerable efficiencies in summarizing, coding and socializing qualitative research outputs. AI-enabled research repositories allow code frames to evolve naturally throughout the analysis process and can be used to identify connections that may otherwise go missed given the scale of qualitative datasets and can be used to quickly generate sharable highlight reels. The efficiencies gained by applying LLM-enabled summarization and tagging to qualitative studies opens the door to not only to elicit qualitatively rich feedback from larger audiences than previously possible, but also to streamline analysis time — making robust qual a possibility even when timelines are tight.
Unlocking new efficiencies in the development of survey instruments
AI tools like ChatGPT are great brainstorming partners once the heavy lifting of research and survey design has been completed. While it will not generate an effective survey in full, they are incredibly helpful to use when pressure-testing questions and response sets. By providing AI tools with consumer and category context, they can do great job in acting as a second set of ‘eyes’ on verbiage to refine instruments based on measures that may be missing or duplicative.
Conclusion
Widespread access to AI tools has transformed the landscape of Market Research in such a short time. What remains unchanged is the fundamental purpose of primary research — to generate fresh human insights that effectively inform business strategies. AI undoubtedly enhances researchers’ efficiencies, creativity and unlock scale in methods; however, it will not replace the critical thinking and strategic lens that are required of effective Researchers to design, field, analyze and socialize insights within the context of marketing challenges. Balance will be key in adapting to this new playing field where Researchers should embrace and experiment with AI to generate fresh insights without losing the human touch that defines the field.
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